Deep learning methods for 3D shape analysis and understanding can be broadly divided into view-based, volumetric and point cloud-based categories.
View-based techniques [1]}, [2]}, [3]} map 3D models into 2D view scenes and then employ image-based CNNs for further analysis. Self-occlusions and information loss often occur during mapping. Volumetric methods [4]}, [5]}, [6]}, [7]}, [8]} quantize the input 3D models into a regular grid before applying 3D CNNs. Loss of resolution, high memory and computational demands are the main limitations of voxelization.
